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We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals.
Icosahedral discretization of the two-sphere
John R Baumgardner and Paul O Frederickson · 1985
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Discrete differential geometry: An applied introduction, 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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James Atwood and Don Towsley · 2016
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Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein · 2016
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Taco S Cohen and Max Welling · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Volumetric and multi-view cnns for object classification on 3d data
Segmenting and tracking extreme climate events using neural networks
Mayur Mudigonda, Sookyung Kim, Ankur Mahesh, Samira Kahou, Karthik Kashinath, Dean Williams, Vincen Michalski, Travis O’Brien, and Mr Prabhat · 2017
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Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Mr Prabhat, and Chris Pal · 2017
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Im2pano3d: Extrapolating 360 structure and semantics beyond the field of view
Shuran Song, Andy Zeng, Angel X Chang, Manolis Savva, Silvio Savarese, and Thomas Funkhouser · 2017
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Learning spherical convolution for fast features from 360 imagery
Yu-Chuan Su and Kristen Grauman · 2017
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Segcloud: Semantic segmentation of 3d point clouds
Lyne Tchapmi, Christopher Choy, Iro Armeni, JunYoung Gwak, and Silvio Savarese · 2017
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Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas · 2016
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Joint 2d-3d-semantic data for indoor scene understanding
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Geometric deep learning: going beyond euclidean data
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Convolutional neural networks on surfaces via seamless toric covers
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Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
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Spherical CNNs
Taco S. Cohen, Mario Geiger, Jonas Köhler, and Max Welling · 2018
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Spherenet: Learning spherical representations for detection and classification in omnidirectional images
Benjamin Coors, Alexandru Paul Condurache, and Andreas Geiger · 2018
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Learning so (3) equivariant representations with spherical cnns
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Deep neural networks motivated by partial differential equations
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Understanding convolution for semantic segmentation
Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell · 2018
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Distortion-aware cnns for spherical images
Qiang Zhao, Chen Zhu, Feng Dai, Yike Ma, Guoqing Jin, and Yongdong Zhang · 2018
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